CS7641 Machine Learning

Assignment 3 - Unsupervised Learning

Author: Vivek Agrawal

References

1. Data Sets Used

First step in the process is to load all the datasets

  1. https://archive.ics.uci.edu/ml/datasets/Census+Income
  2. https://archive.ics.uci.edu/ml/datasets/Wine+Quality
In [1]:
#Imports
import pandas as pd
import numpy as np
import itertools 
from scipy.stats import kurtosis

from sklearn.metrics import silhouette_samples, silhouette_score
import matplotlib.cm as cm


import matplotlib.pyplot as plt
from time import clock
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.cluster import KMeans
from sklearn.mixture import GaussianMixture
from collections import defaultdict
from sklearn.metrics import adjusted_mutual_info_score as ami
from sklearn.metrics import homogeneity_score, completeness_score, homogeneity_completeness_v_measure
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.decomposition import PCA
from sklearn.decomposition import FastICA
from sklearn.random_projection import SparseRandomProjection
from itertools import product
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.base import TransformerMixin,BaseEstimator
import warnings
warnings.simplefilter('ignore')
from sklearn.preprocessing import scale

Data Cleanup

In [2]:
def gtid():
    return 903471711

def author(self):
    return 'vagrawal63'	

  
def get_income_data(file_name = "adult.csv"):
    data = pd.read_csv(file_name)
    #clean rows with empty values 
    data = data[data.occupation.str.strip() != '?']
    data = data[data.workclass.str.strip() != '?']
    data = data[data.nativecountry.str.strip() != '?']
    #convert income to binary classes
    def func(x):
        if(x == " <=50K" or x == " <=50K."):
            return 0
        elif(x == " >50K" or x == ' >50K.'):
            return 1
    data['income'] = data['income'].apply(func)
    #print(data.head())
    result = data['income']
    del data['income']
    del data['education']
    del data['fnlwgt']
    #testing if I want to delete these additional columns
    # result: Tree size drops from Cleaned: (45222, 486) to (45222, 268)
    # accuracy drops from Accuracy of tree (No pruning):0.823377120460988 to 0.7842997385726206
    del data['capitalgain']
    del data['capitalloss']
    #encode to binary values
    #enc = OneHotEncoder(sparse = False, handle_unknown='ignore')
    #data = enc.fit_transform(data)
    data = pd.get_dummies(data, columns=['workclass','maritalstatus','occupation',
                                         'relationship','race',
                                         'gender','nativecountry'])
    
    #print(enc.categories_)
    data = scale(data)
    return data, result


#randomize using GT ID
np.random.seed(gtid())
#Read Data File
income_data, result = get_income_data()
print("Cleaned: " + str(income_data.shape))
Cleaned: (45222, 85)
In [3]:
print("Split data")
#split data
X_train, X_test, Y_train, Y_test = train_test_split(income_data, result, test_size  = 0.2)
print("X_train: " + str(X_train.shape))
Split data
X_train: (36177, 85)

Part 1: K Means and GMM (Expectation Maximization)

In [4]:
print('Part 1: Start Clustering on Income Data ==')
clusters =  [2,4,6,8,10,12,14,16,18,20,25,30]


SSE = defaultdict(dict)
BIC = defaultdict(dict)
hScore = defaultdict(dict)
cScore = defaultdict(dict)
AMI = defaultdict(dict)
VMeasure = defaultdict(dict)
km = KMeans(random_state=5)
gmm = GaussianMixture(random_state = 100)

st = clock()
for k in clusters:
    km.set_params(n_clusters=k)
    gmm.set_params(n_components=k)
    
    km.fit(X_train)
    gmm.fit(X_train)

    SSE[k]['IncomeInertia'] = (km.inertia_)
    BIC[k]['IncomeBIC'] = gmm.bic(X_train)
    
    hScore[k]['KM'] = homogeneity_score(Y_train,km.predict(X_train))
    hScore[k]['GMM'] = homogeneity_score(Y_train,gmm.predict(X_train))
    
    cScore[k]['KM'] = completeness_score(Y_train,km.predict(X_train))
    cScore[k]['GMM'] = completeness_score(Y_train,gmm.predict(X_train))
    
    AMI[k]['KM'] = ami(Y_train,km.predict(X_train))
    AMI[k]['GMM'] = ami(Y_train,gmm.predict(X_train))
    
    
    a,b,vm = homogeneity_completeness_v_measure(Y_train,km.predict(X_train))
    VMeasure[k]['KM'] = vm
    
    a,b,vm = homogeneity_completeness_v_measure(Y_train,gmm.predict(X_train))
    VMeasure[k]['GMM'] = vm
    
    
SSE = (pd.DataFrame(SSE)).T
BIC = pd.DataFrame(BIC).T

hScore = pd.DataFrame(hScore).T 
cScore = pd.DataFrame(cScore).T
AMI = pd.DataFrame(AMI).T
VMeasure = pd.DataFrame(VMeasure).T

print("Writing files now .. ")
SSE.to_csv('./P1/IncomeClusterKMeans.csv')
BIC.to_csv('./P1/IncomeClusterGMM.csv')

hScore.to_csv('./P1/IncomeHScore.csv')
cScore.to_csv('./P1/IncomeCScore.csv')
AMI.to_csv('./P1/IncomeAMI.csv')
VMeasure.to_csv('./P1/IncomeVMeasure.csv')
print("Finished writing files")
Part 1: Start Clustering on Income Data ==
Writing files now .. 
Finished writing files
In [5]:
def plot_clustering_charts():

    
    KNNCluster = pd.read_csv("./P1/IncomeClusterKMeans.csv", header='infer')
    GMMCluster = pd.read_csv("./P1/IncomeClusterGMM.csv", header='infer')

    HScore = pd.read_csv("./P1/IncomeHScore.csv", header = 'infer')
    CScore = pd.read_csv("./P1/IncomeCScore.csv", header = 'infer')
    AMI = pd.read_csv("./P1/IncomeAMI.csv", header = 'infer')

    VMeasure = pd.read_csv("./P1/IncomeVMeasure.csv", header = 'infer')

    x_data = HScore['Unnamed: 0']
    plt.close()
    plt.plot(x_data, KNNCluster['IncomeInertia'], 'bx-', color = 'blue', linewidth = 1, label = "Number of Clusters" )
    plt.axvline(x = 6, linestyle = "--", linewidth = 1, color = "k", label = "Optimal Clusters = 6")
    plt.legend(loc = 'best')
    plt.title("Figure 1.1: KMeans Elbow Method\nIncome Dataset")
    plt.xlabel("Number of Clusters")
    plt.ylabel("Sum of Squared Distances");
    plt.show()
    plt.close()

    plt.plot(x_data, HScore['KM'], color = 'orange',  label = "Homogenity" )
    plt.plot(x_data, CScore['KM'], color = 'blue',  label = "Completeness" )
    plt.plot(x_data, AMI['KM'], color = 'green',  label = "Adjusted MI" )
    plt.plot(x_data, VMeasure['KM'], color = 'red',  label = "V Measure" )
    plt.axvline(x = 6 , linestyle = "--", linewidth = 1, color = "k", label = "Optimal Clusters = 6")

    plt.legend(loc = 'upper right')
    plt.title("Figure 1.2: KMeans Performance Evaluation\nIncome Dataset")
    plt.xlabel("Number of Clusters")
    plt.ylabel("Score");
    plt.show()
    plt.close()

    plt.plot(x_data, GMMCluster['IncomeBIC'], 'bx-', color = 'blue', linewidth = 1, label = "Number of Clusters" )
    plt.axvline(x = 8 , linestyle = "--", linewidth = 1, color = "k", label = "Optimal Clusters = 8")
    plt.legend(loc = 'best')
    plt.title("Figure 2.2: Expectation Maximization BIC\nIncome Dataset")
    plt.xlabel("Number of Clusters")
    plt.ylabel("BIC");
    plt.show()
    plt.close()

    plt.plot(x_data, HScore['GMM'], color = 'orange',  label = "Homogenity" )
    plt.plot(x_data, CScore['GMM'], color = 'blue',  label = "Completeness" )
    plt.plot(x_data, AMI['GMM'], color = 'green',  label = "Adjusted MI" )
    plt.plot(x_data, VMeasure['GMM'], color = 'red',  label = "V Measure" )
    plt.axvline(x = 8 , linestyle = "--", linewidth = 1, color = "k", label = "Optimal Clusters = 8")
    plt.legend(loc = 'upper right')
    plt.title("Figure 2.2: Expectation Maximization Performance Evaluation\nIncome Dataset")
    plt.xlabel("Number of Clusters")
    plt.ylabel("Score");
    plt.show()
    plt.close()
In [6]:
plot_clustering_charts()
In [7]:
def KM_Silhoutte(X, y, title=""):
    if (title == ""):
        title = "Figure 1.3: KMeans Clustering Silhoutte Analysis\nIncome Dataset "
# Generating the sample data from make_blobs
# This particular setting has one distinct cluster and 3 clusters placed close
# together.


    range_n_clusters = [2,4,6,8,10,12,14,16,18,20,25,30]

    for n_clusters in range_n_clusters:
        # Create a subplot with 1 row and 2 columns
        fig, (ax1, ax2) = plt.subplots(1, 2)
        fig.set_size_inches(12,5)

        # The 1st subplot is the silhouette plot
        # The silhouette coefficient can range from -1, 1 but in this example all
        # lie within [-0.1, 1]
        ax1.set_xlim([-0.1, 1])
        # The (n_clusters+1)*10 is for inserting blank space between silhouette
        # plots of individual clusters, to demarcate them clearly.
        ax1.set_ylim([0, len(X) + (n_clusters + 1) * 10])

        # Initialize the clusterer with n_clusters value and a random generator
        # seed of 10 for reproducibility.
        clusterer = KMeans(n_clusters=n_clusters, random_state=10)
        cluster_labels = clusterer.fit_predict(X)

        # The silhouette_score gives the average value for all the samples.
        # This gives a perspective into the density and separation of the formed
        # clusters
        silhouette_avg = silhouette_score(X, cluster_labels)
        print("For n_clusters =", n_clusters,
              "The average silhouette_score is :", silhouette_avg)

        # Compute the silhouette scores for each sample
        sample_silhouette_values = silhouette_samples(X, cluster_labels)

        y_lower = 10
        for i in range(n_clusters):
            # Aggregate the silhouette scores for samples belonging to
            # cluster i, and sort them
            ith_cluster_silhouette_values = \
                sample_silhouette_values[cluster_labels == i]

            ith_cluster_silhouette_values.sort()

            size_cluster_i = ith_cluster_silhouette_values.shape[0]
            y_upper = y_lower + size_cluster_i

            color = cm.nipy_spectral(float(i) / n_clusters)
            ax1.fill_betweenx(np.arange(y_lower, y_upper),
                              0, ith_cluster_silhouette_values,
                              facecolor=color, edgecolor=color, alpha=0.7)

            # Label the silhouette plots with their cluster numbers at the middle
            ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))

            # Compute the new y_lower for next plot
            y_lower = y_upper + 10  # 10 for the 0 samples

        #ax1.set_title("Figure 1c: kMeans silhouette plot (Cancer Dataset)")
        ax1.set_xlabel("Silhouette coefficient values")
        #ax1.set_ylabel("Cluster label")

        # The vertical line for average silhouette score of all the values
        ax1.axvline(x=silhouette_avg, color="red", linestyle="--", label="Average Silhouette Score")

        #ax1.set_yticks([])  # Clear the yaxis labels / ticks
        #ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])

        # 2nd Plot showing the actual clusters formed
        colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters)
        ax2.scatter(X[:, 0], X[:, 1], marker='.', s=30, lw=0, alpha=0.7,
                    c=colors, edgecolor='k')

        # Labeling the clusters
        centers = clusterer.cluster_centers_
        # Draw white circles at cluster centers
        ax2.scatter(centers[:, 0], centers[:, 1], marker='o',
                    c="white", alpha=1, s=200, edgecolor='k')

        for i, c in enumerate(centers):
            ax2.scatter(c[0], c[1], marker='$%d$' % i, alpha=1,
                        s=50, edgecolor='k')

        plt.suptitle((title+
                      "(No of Clusters = %d)" % n_clusters),
                     fontsize=14)

    plt.show()
In [8]:
KM_Silhoutte(X_train, Y_train)
For n_clusters = 2 The average silhouette_score is : 0.08115581078907247
For n_clusters = 4 The average silhouette_score is : 0.10272281993615101
For n_clusters = 6 The average silhouette_score is : 0.07091988464127319
For n_clusters = 8 The average silhouette_score is : 0.0783204371498665
For n_clusters = 10 The average silhouette_score is : 0.06736107980806884
For n_clusters = 12 The average silhouette_score is : 0.08830071308883625
For n_clusters = 14 The average silhouette_score is : 0.10042991998709964
For n_clusters = 16 The average silhouette_score is : 0.0990647039652664
For n_clusters = 18 The average silhouette_score is : 0.09380364800221486
For n_clusters = 20 The average silhouette_score is : 0.10985374451896808
For n_clusters = 25 The average silhouette_score is : 0.11028857569880582
For n_clusters = 30 The average silhouette_score is : 0.10099765925463926
In [9]:
def compute_bic_score(X,title1, title2):
    lowest_bic = np.infty
    bic = []
    n_components_range = [2,4,6,8,10,12,14,16,18,20,25,30]
    cv_types = ['spherical', 'tied', 'diag', 'full']
    for cv_type in cv_types:
        for n_components in n_components_range:
            # Fit a Gaussian mixture with EM
            gmm = GaussianMixture(n_components=n_components,
                                          covariance_type=cv_type)
            gmm.fit(X)
            bic.append(gmm.bic(X))
            print("CV Type: ", cv_type, " Components: ", n_components, " BIC Score: ", bic[-1])
            if bic[-1] < lowest_bic:
                lowest_bic = bic[-1]
                best_gmm = gmm

    bic = np.array(bic)
    color_iter = itertools.cycle(['navy', 'turquoise', 'cornflowerblue',
                                  'darkorange'])

    print("Lowest BIC score = ", lowest_bic)
    # Plot the BIC scores
    plt.figure(figsize=(8, 6))
    clf = best_gmm
    bars = []
    spl = plt.subplot(2, 1, 1)
    for i, (cv_type, color) in enumerate(zip(cv_types, color_iter)):
        xpos = np.array(n_components_range) + .2 * (i - 2)
        bars.append(plt.bar(xpos, bic[i * len(n_components_range):
                                      (i + 1) * len(n_components_range)],
                            width=.2, color=color))
    plt.xticks(n_components_range)
    plt.ylim([bic.min() * 1.01 - .01 * bic.max(), bic.max()])
    plt.title(title1)
    xpos = np.mod(bic.argmin(), len(n_components_range)) + .65 +\
        .2 * np.floor(bic.argmin() / len(n_components_range))
    plt.text(xpos, bic.min() * 0.97 + .03 * bic.max(), '*', fontsize=14)
    spl.set_xlabel('Number of components')
    spl.legend([b[0] for b in bars], cv_types)
    #plt.show()
    #plt.close()

    # Plot the winner
    #4splot = plt.subplot(2, 1, 2)
    Y_ = clf.predict(X)
    plt.show()
    plt.close()
In [10]:
compute_bic_score(X_train, "Figure 2.1: Expectation Maximization BIC Score\nIncome Dataset" , "Figure 2.2: Cluster Representation\nIncome Dataset")
CV Type:  spherical  Components:  2  BIC Score:  6986357.822452916
CV Type:  spherical  Components:  4  BIC Score:  6434361.585392872
CV Type:  spherical  Components:  6  BIC Score:  6516814.151453002
CV Type:  spherical  Components:  8  BIC Score:  6084228.923079217
CV Type:  spherical  Components:  10  BIC Score:  5762750.719819664
CV Type:  spherical  Components:  12  BIC Score:  5720984.73560336
CV Type:  spherical  Components:  14  BIC Score:  5845278.291031575
CV Type:  spherical  Components:  16  BIC Score:  5814403.920487542
CV Type:  spherical  Components:  18  BIC Score:  5557846.257856089
CV Type:  spherical  Components:  20  BIC Score:  5724045.86817732
CV Type:  spherical  Components:  25  BIC Score:  5533624.2194878
CV Type:  spherical  Components:  30  BIC Score:  4992626.243944958
CV Type:  tied  Components:  2  BIC Score:  4443030.446435416
CV Type:  tied  Components:  4  BIC Score:  4295826.503749669
CV Type:  tied  Components:  6  BIC Score:  4656025.341227304
CV Type:  tied  Components:  8  BIC Score:  2937270.0103856977
CV Type:  tied  Components:  10  BIC Score:  1601168.68235255
CV Type:  tied  Components:  12  BIC Score:  2629259.741099345
CV Type:  tied  Components:  14  BIC Score:  725136.7080060458
CV Type:  tied  Components:  16  BIC Score:  1579812.9839806214
CV Type:  tied  Components:  18  BIC Score:  46965.4972728387
CV Type:  tied  Components:  20  BIC Score:  -147660.81256293668
CV Type:  tied  Components:  25  BIC Score:  -2088045.315300413
CV Type:  tied  Components:  30  BIC Score:  -4294773.471039979
CV Type:  diag  Components:  2  BIC Score:  8039116.821840139
CV Type:  diag  Components:  4  BIC Score:  -125571.64819110215
CV Type:  diag  Components:  6  BIC Score:  -4522064.606861852
CV Type:  diag  Components:  8  BIC Score:  -9676384.758504674
CV Type:  diag  Components:  10  BIC Score:  -2582571.3004599446
CV Type:  diag  Components:  12  BIC Score:  -13314191.234770233
CV Type:  diag  Components:  14  BIC Score:  -4561076.640439638
CV Type:  diag  Components:  16  BIC Score:  -10011690.124383181
CV Type:  diag  Components:  18  BIC Score:  -15276641.511928806
CV Type:  diag  Components:  20  BIC Score:  -11398187.600016959
CV Type:  diag  Components:  25  BIC Score:  -11840548.919495095
CV Type:  diag  Components:  30  BIC Score:  -10692287.231013007
CV Type:  full  Components:  2  BIC Score:  2274996.9475946077
CV Type:  full  Components:  4  BIC Score:  -2328061.1720435675
CV Type:  full  Components:  6  BIC Score:  -4396561.999375262
CV Type:  full  Components:  8  BIC Score:  -14623674.174679583
CV Type:  full  Components:  10  BIC Score:  -19620935.228373975
CV Type:  full  Components:  12  BIC Score:  -10996908.48428336
CV Type:  full  Components:  14  BIC Score:  -7592777.625265727
CV Type:  full  Components:  16  BIC Score:  -12177894.082685187
CV Type:  full  Components:  18  BIC Score:  -18229994.809218112
CV Type:  full  Components:  20  BIC Score:  -13193306.098539768
CV Type:  full  Components:  25  BIC Score:  -11691504.901464531
CV Type:  full  Components:  30  BIC Score:  -13281408.258502735
Lowest BIC score =  -19620935.228373975

Part 2: Dimensionality Reduction (PCA, ICA, RP and SVD)

In [24]:
dimensions = range(1, 85)
ann_learning_rate = [0.05]
ann_hidden_layers = [(8)]

def run_ann(dimensions, classifier, X, Y):
    grid ={'clf__n_components':dimensions,'NN__learning_rate_init':ann_learning_rate,'NN__hidden_layer_sizes':ann_hidden_layers}      
    ann = MLPClassifier(activation='logistic',max_iter=2000,early_stopping=True,random_state=5)
    pipe = Pipeline([('clf',classifier),('NN',ann)])
    gs = GridSearchCV(pipe,grid,verbose=2,cv=5)
    gs.fit(X, Y)
    return (pd.DataFrame(gs.cv_results_) , gs.best_estimator_)
In [25]:
print('Part 2: PCA for Income dataset')
pca = PCA(random_state = 5)
pca.fit_transform(X_train)
EVR = pd.Series(data = pca.explained_variance_ratio_,index = range(0,85))
EVR.to_csv('./P2/IncomePCA-EVR.csv')
EV = pd.Series(data = pca.explained_variance_,index = range(0,85))
EV.to_csv('./P2/IncomePCA-EV.csv')
pca = PCA(random_state = 5)  
nn_results, clf = run_ann(dimensions, pca, X_train, Y_train)     
nn_results.to_csv('./P2/IncomePCA_ANN.csv')
test_score = clf.score(X_test, Y_test)
Part 2: PCA for Income dataset
Fitting 5 folds for each of 84 candidates, totalling 420 fits
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1 
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1 
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.3s remaining:    0.0s
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1, total=   0.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1, total=   0.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1, total=   0.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4, total=   0.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5, total=   0.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7, total=   0.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8, total=   0.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8, total=   0.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9, total=   0.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10, total=   0.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10, total=   0.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12, total=   0.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12, total=   0.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13, total=   0.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14, total=   0.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14, total=   0.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14, total=   0.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15, total=   0.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16, total=   0.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16, total=   0.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17, total=   0.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17, total=   0.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18, total=   0.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19, total=   0.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19, total=   0.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19, total=   0.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21, total=   0.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21, total=   0.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21, total=   0.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22, total=   0.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22, total=   2.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23, total=   0.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25, total=   0.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25, total=   0.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26, total=   0.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26, total=   2.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27, total=   0.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28, total=   0.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28, total=   0.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30, total=   2.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30, total=   0.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31, total=   2.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32, total=   0.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32, total=   2.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33, total=   0.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33, total=   2.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34, total=   2.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36, total=   0.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37, total=   2.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38, total=   2.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38, total=   2.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39, total=   2.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41, total=   2.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42, total=   2.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42, total=   2.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44, total=   2.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45, total=   2.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46, total=   2.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47, total=   2.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48, total=   2.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48, total=   2.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49, total=   2.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51, total=   2.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55, total=   2.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56, total=   2.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57, total=   2.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57, total=   2.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57, total=   2.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58, total=   2.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58, total=   2.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58, total=   2.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58, total=   2.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59, total=   2.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59, total=   2.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59, total=   2.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60, total=   2.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60, total=   2.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61, total=   2.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61, total=   2.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61, total=   2.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61, total=   2.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62, total=   2.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62, total=   2.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62, total=   2.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63, total=   2.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63, total=   2.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63, total=   3.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63, total=   2.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64, total=   2.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64, total=   2.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64, total=   2.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66, total=   2.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69, total=   2.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71, total=   2.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73, total=   2.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78, total=   2.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79, total=   2.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81, total=   2.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84, total=   2.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84, total=   1.7s
[Parallel(n_jobs=1)]: Done 420 out of 420 | elapsed: 10.6min finished
Test Accuracy  :  0.8308457711442786
Best Estimator :  Pipeline(memory=None,
         steps=[('clf',
                 PCA(copy=True, iterated_power='auto', n_components=43,
                     random_state=5, svd_solver='auto', tol=0.0,
                     whiten=False)),
                ('NN',
                 MLPClassifier(activation='logistic', alpha=0.0001,
                               batch_size='auto', beta_1=0.9, beta_2=0.999,
                               early_stopping=True, epsilon=1e-08,
                               hidden_layer_sizes=8, learning_rate='constant',
                               learning_rate_init=0.05, max_iter=2000,
                               momentum=0.9, n_iter_no_change=10,
                               nesterovs_momentum=True, power_t=0.5,
                               random_state=5, shuffle=True, solver='adam',
                               tol=0.0001, validation_fraction=0.1,
                               verbose=False, warm_start=False))],
         verbose=False)
In [26]:
EVR.plot( ylim = (0.,0.35), color = 'red',  label = "Variance Ratio" )

ax = EV.plot(kind = 'bar',ylim = (0.,1.0),label = "Eigen Values")

ticks = ax.xaxis.get_ticklocs()
ticklabels = [l.get_text() for l in ax.xaxis.get_ticklabels()]
ax.xaxis.set_ticks(ticks[::10])
ax.xaxis.set_ticklabels(ticklabels[::10]);
ax.axvline(x = 48 , linestyle = "--", linewidth = 1, color = "k", label = "Optimal Components")

plt.legend(loc=1)
plt.title("Figure 3.1: PCA Eigen Values\nIncome Dataset")
plt.xlabel("Principal Components")
plt.ylabel("Variance Ratio");
plt.show()
plt.close()

print("Reduced Dimension: {} out of {}".
      format(X_train.shape[1]-len([i for i in EVR if i >= 0.025]),X_train.shape[1]))
print("Variance captured: {} %".format(sum([i for i in EVR if i >= 0.025])*100.))

nn_pca = pd.read_csv("./P2/IncomePCA_ANN.csv", header = 'infer')
nn_pca = nn_results['mean_test_score'] * 100.0

nn_pca.plot( color = 'blue',  label = "ANN Accuracy" )
plt.axvline(x = 48 , linestyle = "--", linewidth = 1, color = "k", label = "Optimal Components (n=48)")

plt.legend(loc='best')
plt.title("Figure 3.2: ANN Accuracy Dimension Reduction using PCA\nIncome Dataset")
plt.xlabel("Principal Components")
plt.ylabel("Accuracy Percentage");
plt.show()
plt.close()
Reduced Dimension: 82 out of 85
Variance captured: 11.169802345352783 %
In [27]:
ica = FastICA(random_state=5)
temp = ica.fit_transform(X_train)
order = [-abs(kurtosis(temp[:,i])) for i in range(temp.shape[1])]
temp = temp[:,np.array(order).argsort()]
kurt =  pd.Series([abs(kurtosis(temp[:,i])) for i in range(temp.shape[1])]);

ica = FastICA(random_state=5)  
nn_results , clf = run_ann(dimensions, ica, X_train, Y_train)     
nn_results.to_csv('./P2/IncomeICA_ANN.csv')
test_score = clf.score(X_test, Y_test)
Fitting 5 folds for each of 84 candidates, totalling 420 fits
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1 
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1 
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.5s remaining:    0.0s
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1, total=   2.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=1, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=2, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=3, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=4, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=5, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=6, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=7, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=8, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=9, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10, total=   2.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=10, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=11, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12, total=   1.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=12, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=13, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14, total=   1.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=14, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=15, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=16, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=17, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18, total=   2.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=18, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=19, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20, total=   2.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=20, total=   1.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=21, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22, total=   1.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=22, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23, total=   2.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=23, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24, total=   2.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=24, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25, total=   2.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=25, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26, total=   1.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=26, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27, total=   2.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27, total=   2.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=27, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28, total=   2.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=28, total=   1.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29, total=   2.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29, total=   2.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29, total=   2.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=29, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30, total=   2.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=30, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31, total=   2.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31, total=   2.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31, total=   2.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=31, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32, total=   2.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=32, total=   2.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=33, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34, total=   2.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34, total=   2.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=34, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35, total=   2.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35, total=   1.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35, total=   2.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35, total=   2.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=35, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36, total=   3.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36, total=   2.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36, total=   2.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=36, total=   2.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37, total=   2.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37, total=   2.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37, total=   2.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=37, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38, total=   2.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38, total=   2.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=38, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39, total=   2.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39, total=   2.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39, total=   2.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39, total=   1.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=39, total=   1.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40, total=   2.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40, total=   2.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40, total=   2.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=40, total=   2.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41, total=   2.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41, total=   1.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41, total=   3.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41, total=   2.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=41, total=   2.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42, total=   2.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42, total=   2.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42, total=   2.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42, total=   2.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=42, total=   2.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43, total=   2.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43, total=   2.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=43, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44, total=   2.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44, total=   2.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44, total=   2.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44, total=   2.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=44, total=   2.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45, total=   2.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45, total=   2.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45, total=   3.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=45, total=   2.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46, total=   2.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46, total=   2.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46, total=   2.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=46, total=   2.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47, total=   2.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47, total=   2.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47, total=   2.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=47, total=   2.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48, total=   3.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48, total=   2.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48, total=   2.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48, total=   3.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=48, total=   2.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49, total=   2.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=49, total=   2.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50, total=   2.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50, total=   2.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50, total=   2.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50, total=   2.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=50, total=   3.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51, total=   4.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51, total=   2.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=51, total=   3.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52, total=   2.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52, total=   2.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52, total=   2.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=52, total=   2.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53, total=   3.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53, total=   2.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=53, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54, total=   2.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54, total=   4.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54, total=   2.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=54, total=   2.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55, total=   2.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55, total=   2.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=55, total=   2.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56, total=   2.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56, total=   3.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=56, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57, total=   2.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57, total=   2.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57, total=   4.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=57, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58, total=   2.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58, total=   2.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=58, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59, total=   4.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59, total=   4.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59, total=   2.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59, total=   2.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=59, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60, total=   3.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60, total=   3.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=60, total=   4.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61, total=   4.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61, total=   4.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=61, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62, total=   2.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62, total=   3.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=62, total=   2.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63, total=   4.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63, total=   5.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=63, total=   2.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64, total=   4.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64, total=   2.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=64, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65, total=   4.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=65, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66, total=   4.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=66, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67, total=   4.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67, total=   4.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=67, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68, total=   4.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68, total=   4.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=68, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69, total=   4.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69, total=   4.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69, total=   4.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=69, total=   4.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=70, total=   4.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=71, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72, total=   4.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72, total=   5.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=72, total=   3.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73, total=   4.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=73, total=   4.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74, total=   4.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74, total=   4.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=74, total=   5.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75, total=   4.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75, total=   4.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75, total=   4.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=75, total=   4.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76, total=   4.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76, total=   4.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=76, total=   4.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77, total=   4.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77, total=   4.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77, total=   4.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77, total=   4.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=77, total=   4.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78, total=   4.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78, total=   4.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=78, total=   5.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79, total=   4.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79, total=   4.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79, total=   4.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79, total=   4.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=79, total=   8.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80, total=   8.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80, total=   8.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80, total=   8.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80, total=   8.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=80, total=   8.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81, total=   8.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81, total=   8.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81, total=   8.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81, total=   8.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=81, total=   9.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82, total=   8.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82, total=   8.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82, total=   8.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82, total=   8.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=82, total=   8.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83, total=   8.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83, total=   8.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83, total=   8.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83, total=   8.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=83, total=   9.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84, total=   8.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84, total=   8.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84, total=   8.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84, total=   8.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, clf__n_components=84, total=   8.3s
[Parallel(n_jobs=1)]: Done 420 out of 420 | elapsed: 20.2min finished
Test Accuracy  :  0.8323935876174682
Best Estimator :  Pipeline(memory=None,
         steps=[('clf',
                 FastICA(algorithm='parallel', fun='logcosh', fun_args=None,
                         max_iter=200, n_components=81, random_state=5,
                         tol=0.0001, w_init=None, whiten=True)),
                ('NN',
                 MLPClassifier(activation='logistic', alpha=0.0001,
                               batch_size='auto', beta_1=0.9, beta_2=0.999,
                               early_stopping=True, epsilon=1e-08,
                               hidden_layer_sizes=8, learning_rate='constant',
                               learning_rate_init=0.05, max_iter=2000,
                               momentum=0.9, n_iter_no_change=10,
                               nesterovs_momentum=True, power_t=0.5,
                               random_state=5, shuffle=True, solver='adam',
                               tol=0.0001, validation_fraction=0.1,
                               verbose=False, warm_start=False))],
         verbose=False)
In [28]:
plt.figure(figsize=(6,4))
ax = kurt.plot(kind = 'bar', label = "Kurtosis Disribution");
ticks = ax.xaxis.get_ticklocs()
ticklabels = [l.get_text() for l in ax.xaxis.get_ticklabels()]
ax.xaxis.set_ticks(ticks[::10])
ax.xaxis.set_ticklabels(ticklabels[::10]);
ax.axvline(x=33 , linestyle = "--", linewidth = 1, color = "k", label = "Optimal Components")


plt.legend(loc='best')
plt.title("Figure 4.1: ICA - Kurtosis\nIncome Dataset")
plt.xlabel("Independent Components")
plt.ylabel("Kurtosis");
plt.show()
plt.close()
print("Reduced Dimension: {} out of {}".format(X_train.shape[1]-len([i for i in kurt if i >= 8.]),
                                               X_train.shape[1]))
nn_ica = pd.read_csv("./P2/IncomeICA_ANN.csv", header = 'infer')
nn_ica = nn_results['mean_test_score'] * 100.0
plt.axvline(x=33 , linestyle = "--", linewidth = 1, color = "k", label = "Optimal Components (n=33)")
nn_ica.plot( color = 'blue',  label = "ANN Accuracy" )
plt.legend(loc='best')
plt.title("Figure 4.2: ANN Accuracy Dimension Reduction using ICA\nIncome Dataset")
plt.xlabel("Independent Components")
plt.ylabel("Accuracy Percentage");
plt.show()
plt.close()
Reduced Dimension: 15 out of 85
In [ ]:
def distance_correlation (X1,X2):
    assert X1.shape[0] == X2.shape[0]
    return np.corrcoef(pairwise_distances(X1).ravel(),pairwise_distances(X2).ravel())[0,1]
In [ ]:
tmp = defaultdict(dict)
for i,dim in product(range(10),dimensions):
    rp = SparseRandomProjection(random_state=i, n_components=dim)
    tmp[dim][i] = distance_correlation(rp.fit_transform(X_train), X_train)

tmp = pd.DataFrame(tmp).T
tmp.to_csv('./P2/IncomeRP_DistanceCorrelation.csv')
In [ ]:
# Run Neural Networks
rp = SparseRandomProjection(random_state=5) 
nn_results, clf = run_ann(dimensions, rp,  X_train, Y_train)     
nn_results.to_csv('./P2/IncomeRP_ANN.csv')

## test score
test_score = clf.score(X_test, Y_test)
print("Test Accuracy = ", test_score )
print("Best Estimator = ", clf)
In [ ]:
tmp['mean'] = tmp.mean(axis=1)

distance = tmp['mean']*100.0

distance.plot(color = 'blue',  label = "Distance Correlation" )
plt.axvline(x=8 , linestyle = "--", linewidth = 1, color = "k", label = "Optimal Features")

plt.legend(loc='best')
plt.title("Figure 5.1: Random Projection Distance Correlation\nIncome Dataset")
plt.xlabel("Random Components")
plt.ylabel("Distance Correlation");
plt.show()
plt.close()

nn_results = pd.read_csv("./P2/IncomeRP_ANN.csv", header = 'infer')
nn_results = nn_results['mean_test_score'] * 100.0
nn_results.plot( color = 'blue',  label = "Accuracy Percentage" )
plt.axvline(x=8 , linestyle = "--", linewidth = 1, color = "k", label = "Optimal Features (n=8)")

plt.legend(loc='best')
plt.title("Figure 5.2: ANN Accuracy Dimension Reduction using RP\nIncome Dataset")
plt.xlabel("Random Components")
plt.ylabel("Accuracy Percentage");
plt.show()
plt.close()
In [16]:
class ImportanceSelect(BaseEstimator, TransformerMixin):
    def __init__(self, model, n=1):
         self.model = model
         self.n = n
    def fit(self, *args, **kwargs):
         self.model.fit(*args, **kwargs)
         return self
    def transform(self, X):
         return X[:,self.model.feature_importances_.argsort()[::-1][:self.n]]
In [17]:
rfc = RandomForestClassifier(n_estimators=100, class_weight='balanced', random_state=5, n_jobs=-1)
result = rfc.fit(X_train, Y_train).feature_importances_ 
tmp = pd.Series(np.sort(result)[::-1])
tmp.to_csv('./P2/IncomeRF_FI.csv')
In [18]:
ann_learning_rate = [0.05]
ann_hidden_layers = [(8)]

rfc = RandomForestClassifier(n_estimators=100,class_weight='balanced',random_state=5,n_jobs=-1)
filtr = ImportanceSelect(rfc)
grid ={'filter__n':dimensions,'NN__learning_rate_init':ann_learning_rate,'NN__hidden_layer_sizes':ann_hidden_layers}  
ann = MLPClassifier(activation='logistic',max_iter=2000,early_stopping=True,random_state=5)
pipe = Pipeline([('filter',filtr),('NN',ann)])
gs = GridSearchCV(pipe,grid,verbose=10,cv=5)
gs.fit(X_train, Y_train)
nn_results = pd.DataFrame(gs.cv_results_)
nn_results.to_csv('./P2/IncomeRF_ANN.csv')
Fitting 5 folds for each of 84 candidates, totalling 420 fits
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=1 
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=1, score=0.752, total=   4.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=1 
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    4.7s remaining:    0.0s
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=1, score=0.752, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=1 
[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    7.9s remaining:    0.0s
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=1, score=0.752, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=1 
[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:   11.3s remaining:    0.0s
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=1, score=0.752, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=1 
[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:   14.6s remaining:    0.0s
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=1, score=0.752, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=2 
[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:   17.7s remaining:    0.0s
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=2, score=0.788, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=2 
[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:   20.9s remaining:    0.0s
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=2, score=0.777, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=2 
[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:   24.4s remaining:    0.0s
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=2, score=0.779, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=2 
[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:   28.1s remaining:    0.0s
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=2, score=0.787, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=2 
[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:   31.6s remaining:    0.0s
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=2, score=0.788, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=3 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=3, score=0.795, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=3 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=3, score=0.789, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=3 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=3, score=0.785, total=   3.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=3 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=3, score=0.791, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=3 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=3, score=0.798, total=   3.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=4 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=4, score=0.823, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=4 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=4, score=0.821, total=   3.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=4 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=4, score=0.826, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=4 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=4, score=0.821, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=4 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=4, score=0.829, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=5 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=5, score=0.823, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=5 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=5, score=0.818, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=5 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=5, score=0.827, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=5 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=5, score=0.825, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=5 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=5, score=0.829, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=6 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=6, score=0.820, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=6 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=6, score=0.819, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=6 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=6, score=0.824, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=6 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=6, score=0.825, total=   3.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=6 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=6, score=0.833, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=7 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=7, score=0.827, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=7 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=7, score=0.820, total=   3.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=7 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=7, score=0.822, total=   3.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=7 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=7, score=0.822, total=   3.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=7 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=7, score=0.832, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=8 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=8, score=0.828, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=8 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=8, score=0.828, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=8 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=8, score=0.829, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=8 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=8, score=0.818, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=8 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=8, score=0.835, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=9 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=9, score=0.827, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=9 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=9, score=0.821, total=   3.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=9 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=9, score=0.824, total=   2.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=9 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=9, score=0.820, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=9 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=9, score=0.835, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=10 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=10, score=0.828, total=   3.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=10 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=10, score=0.827, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=10 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=10, score=0.824, total=   3.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=10 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=10, score=0.818, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=10 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=10, score=0.836, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=11 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=11, score=0.822, total=   3.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=11 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=11, score=0.826, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=11 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=11, score=0.819, total=   2.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=11 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=11, score=0.821, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=11 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=11, score=0.833, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=12 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=12, score=0.830, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=12 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=12, score=0.826, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=12 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=12, score=0.825, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=12 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=12, score=0.820, total=   4.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=12 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=12, score=0.835, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=13 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=13, score=0.831, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=13 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=13, score=0.827, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=13 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=13, score=0.829, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=13 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=13, score=0.824, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=13 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=13, score=0.836, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=14 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=14, score=0.826, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=14 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=14, score=0.828, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=14 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=14, score=0.828, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=14 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=14, score=0.822, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=14 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=14, score=0.835, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=15 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=15, score=0.828, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=15 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=15, score=0.828, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=15 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=15, score=0.826, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=15 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=15, score=0.823, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=15 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=15, score=0.837, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=16 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=16, score=0.828, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=16 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=16, score=0.827, total=   4.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=16 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=16, score=0.826, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=16 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=16, score=0.827, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=16 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=16, score=0.836, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=17 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=17, score=0.830, total=   4.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=17 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=17, score=0.824, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=17 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=17, score=0.824, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=17 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=17, score=0.828, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=17 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=17, score=0.835, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=18 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=18, score=0.832, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=18 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=18, score=0.822, total=   3.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=18 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=18, score=0.827, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=18 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=18, score=0.825, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=18 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=18, score=0.837, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=19 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=19, score=0.829, total=   3.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=19 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=19, score=0.829, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=19 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=19, score=0.828, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=19 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=19, score=0.827, total=   4.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=19 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=19, score=0.839, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=20 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=20, score=0.832, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=20 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=20, score=0.825, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=20 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=20, score=0.830, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=20 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=20, score=0.826, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=20 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=20, score=0.839, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=21 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=21, score=0.830, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=21 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=21, score=0.827, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=21 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=21, score=0.830, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=21 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=21, score=0.829, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=21 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=21, score=0.836, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=22 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=22, score=0.830, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=22 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=22, score=0.827, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=22 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=22, score=0.829, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=22 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=22, score=0.829, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=22 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=22, score=0.837, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=23 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=23, score=0.829, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=23 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=23, score=0.824, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=23 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=23, score=0.831, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=23 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=23, score=0.822, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=23 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=23, score=0.839, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=24 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=24, score=0.828, total=   4.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=24 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=24, score=0.823, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=24 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=24, score=0.830, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=24 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=24, score=0.828, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=24 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=24, score=0.837, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=25 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=25, score=0.833, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=25 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=25, score=0.823, total=   4.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=25 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=25, score=0.828, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=25 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=25, score=0.826, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=25 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=25, score=0.835, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=26 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=26, score=0.830, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=26 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=26, score=0.829, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=26 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=26, score=0.831, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=26 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=26, score=0.826, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=26 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=26, score=0.837, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=27 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=27, score=0.830, total=   4.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=27 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=27, score=0.823, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=27 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=27, score=0.828, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=27 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=27, score=0.827, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=27 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=27, score=0.831, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=28 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=28, score=0.832, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=28 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=28, score=0.828, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=28 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=28, score=0.827, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=28 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=28, score=0.826, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=28 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=28, score=0.832, total=   4.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=29 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=29, score=0.833, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=29 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=29, score=0.828, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=29 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=29, score=0.831, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=29 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=29, score=0.829, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=29 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=29, score=0.832, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=30 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=30, score=0.828, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=30 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=30, score=0.824, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=30 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=30, score=0.831, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=30 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=30, score=0.825, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=30 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=30, score=0.836, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=31 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=31, score=0.832, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=31 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=31, score=0.825, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=31 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=31, score=0.831, total=   4.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=31 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=31, score=0.825, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=31 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=31, score=0.838, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=32 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=32, score=0.832, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=32 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=32, score=0.827, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=32 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=32, score=0.832, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=32 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=32, score=0.826, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=32 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=32, score=0.838, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=33 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=33, score=0.833, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=33 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=33, score=0.825, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=33 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=33, score=0.833, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=33 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=33, score=0.831, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=33 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=33, score=0.837, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=34 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=34, score=0.832, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=34 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=34, score=0.825, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=34 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=34, score=0.831, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=34 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=34, score=0.828, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=34 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=34, score=0.838, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=35 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=35, score=0.833, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=35 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=35, score=0.826, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=35 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=35, score=0.830, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=35 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=35, score=0.827, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=35 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=35, score=0.837, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=36 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=36, score=0.830, total=   4.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=36 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=36, score=0.826, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=36 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=36, score=0.831, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=36 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=36, score=0.825, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=36 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=36, score=0.840, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=37 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=37, score=0.833, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=37 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=37, score=0.822, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=37 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=37, score=0.828, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=37 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=37, score=0.827, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=37 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=37, score=0.836, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=38 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=38, score=0.830, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=38 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=38, score=0.825, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=38 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=38, score=0.829, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=38 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=38, score=0.822, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=38 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=38, score=0.837, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=39 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=39, score=0.833, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=39 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=39, score=0.826, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=39 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=39, score=0.832, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=39 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=39, score=0.829, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=39 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=39, score=0.840, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=40 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=40, score=0.833, total=   4.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=40 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=40, score=0.824, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=40 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=40, score=0.831, total=   4.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=40 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=40, score=0.824, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=40 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=40, score=0.831, total=   4.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=41 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=41, score=0.830, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=41 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=41, score=0.827, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=41 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=41, score=0.833, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=41 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=41, score=0.832, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=41 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=41, score=0.835, total=   4.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=42 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=42, score=0.829, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=42 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=42, score=0.826, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=42 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=42, score=0.830, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=42 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=42, score=0.829, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=42 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=42, score=0.835, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=43 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=43, score=0.835, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=43 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=43, score=0.826, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=43 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=43, score=0.829, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=43 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=43, score=0.830, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=43 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=43, score=0.835, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=44 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=44, score=0.830, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=44 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=44, score=0.826, total=   4.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=44 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=44, score=0.833, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=44 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=44, score=0.829, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=44 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=44, score=0.839, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=45 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=45, score=0.830, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=45 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=45, score=0.827, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=45 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=45, score=0.829, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=45 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=45, score=0.828, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=45 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=45, score=0.835, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=46 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=46, score=0.825, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=46 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=46, score=0.827, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=46 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=46, score=0.831, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=46 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=46, score=0.829, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=46 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=46, score=0.835, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=47 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=47, score=0.832, total=   4.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=47 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=47, score=0.828, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=47 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=47, score=0.829, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=47 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=47, score=0.831, total=   4.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=47 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=47, score=0.837, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=48 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=48, score=0.831, total=   4.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=48 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=48, score=0.824, total=   4.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=48 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=48, score=0.827, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=48 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=48, score=0.827, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=48 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=48, score=0.834, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=49 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=49, score=0.836, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=49 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=49, score=0.824, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=49 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=49, score=0.832, total=   4.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=49 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=49, score=0.822, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=49 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=49, score=0.834, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=50 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=50, score=0.831, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=50 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=50, score=0.822, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=50 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=50, score=0.830, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=50 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=50, score=0.830, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=50 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=50, score=0.834, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=51 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=51, score=0.829, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=51 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=51, score=0.825, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=51 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=51, score=0.832, total=   4.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=51 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=51, score=0.830, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=51 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=51, score=0.839, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=52 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=52, score=0.832, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=52 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=52, score=0.825, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=52 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=52, score=0.831, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=52 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=52, score=0.831, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=52 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=52, score=0.837, total=   4.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=53 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=53, score=0.827, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=53 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=53, score=0.824, total=   4.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=53 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=53, score=0.827, total=   4.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=53 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=53, score=0.831, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=53 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=53, score=0.835, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=54 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=54, score=0.829, total=   4.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=54 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=54, score=0.828, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=54 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=54, score=0.829, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=54 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=54, score=0.825, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=54 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=54, score=0.831, total=   4.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=55 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=55, score=0.833, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=55 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=55, score=0.828, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=55 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=55, score=0.831, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=55 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=55, score=0.825, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=55 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=55, score=0.837, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=56 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=56, score=0.831, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=56 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=56, score=0.818, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=56 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=56, score=0.833, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=56 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=56, score=0.822, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=56 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=56, score=0.837, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=57 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=57, score=0.832, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=57 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=57, score=0.825, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=57 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=57, score=0.833, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=57 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=57, score=0.832, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=57 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=57, score=0.835, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=58 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=58, score=0.827, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=58 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=58, score=0.824, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=58 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=58, score=0.825, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=58 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=58, score=0.825, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=58 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=58, score=0.836, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=59 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=59, score=0.829, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=59 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=59, score=0.823, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=59 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=59, score=0.831, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=59 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=59, score=0.829, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=59 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=59, score=0.834, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=60 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=60, score=0.827, total=   4.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=60 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=60, score=0.826, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=60 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=60, score=0.826, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=60 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=60, score=0.829, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=60 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=60, score=0.834, total=   4.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=61 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=61, score=0.830, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=61 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=61, score=0.823, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=61 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=61, score=0.833, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=61 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=61, score=0.828, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=61 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=61, score=0.837, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=62 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=62, score=0.829, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=62 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=62, score=0.826, total=   4.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=62 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=62, score=0.830, total=   4.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=62 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=62, score=0.830, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=62 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=62, score=0.839, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=63 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=63, score=0.829, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=63 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=63, score=0.825, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=63 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=63, score=0.831, total=   4.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=63 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=63, score=0.831, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=63 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=63, score=0.834, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=64 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=64, score=0.836, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=64 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=64, score=0.825, total=   4.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=64 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=64, score=0.835, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=64 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=64, score=0.829, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=64 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=64, score=0.836, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=65 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=65, score=0.829, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=65 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=65, score=0.825, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=65 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=65, score=0.834, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=65 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=65, score=0.831, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=65 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=65, score=0.835, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=66 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=66, score=0.830, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=66 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=66, score=0.824, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=66 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=66, score=0.827, total=   4.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=66 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=66, score=0.826, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=66 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=66, score=0.834, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=67 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=67, score=0.831, total=   4.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=67 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=67, score=0.823, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=67 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=67, score=0.827, total=   4.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=67 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=67, score=0.826, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=67 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=67, score=0.832, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=68 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=68, score=0.829, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=68 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=68, score=0.824, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=68 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=68, score=0.832, total=   3.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=68 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=68, score=0.822, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=68 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=68, score=0.835, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=69 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=69, score=0.831, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=69 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=69, score=0.825, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=69 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=69, score=0.827, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=69 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=69, score=0.826, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=69 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=69, score=0.834, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=70 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=70, score=0.827, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=70 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=70, score=0.828, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=70 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=70, score=0.831, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=70 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=70, score=0.825, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=70 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=70, score=0.834, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=71 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=71, score=0.832, total=   4.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=71 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=71, score=0.821, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=71 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=71, score=0.830, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=71 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=71, score=0.824, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=71 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=71, score=0.835, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=72 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=72, score=0.831, total=   3.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=72 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=72, score=0.825, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=72 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=72, score=0.831, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=72 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=72, score=0.825, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=72 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=72, score=0.832, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=73 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=73, score=0.829, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=73 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=73, score=0.826, total=   4.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=73 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=73, score=0.829, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=73 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=73, score=0.825, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=73 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=73, score=0.833, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=74 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=74, score=0.828, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=74 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=74, score=0.827, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=74 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=74, score=0.831, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=74 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=74, score=0.827, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=74 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=74, score=0.839, total=   4.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=75 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=75, score=0.827, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=75 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=75, score=0.824, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=75 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=75, score=0.828, total=   5.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=75 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=75, score=0.828, total=   5.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=75 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=75, score=0.837, total=   4.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=76 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=76, score=0.833, total=   4.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=76 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=76, score=0.824, total=   4.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=76 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=76, score=0.831, total=   4.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=76 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=76, score=0.825, total=   4.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=76 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=76, score=0.836, total=   4.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=77 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=77, score=0.827, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=77 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=77, score=0.823, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=77 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=77, score=0.830, total=   4.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=77 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=77, score=0.826, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=77 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=77, score=0.838, total=   4.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=78 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=78, score=0.828, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=78 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=78, score=0.824, total=   4.1s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=78 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=78, score=0.833, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=78 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=78, score=0.828, total=   4.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=78 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=78, score=0.832, total=   4.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=79 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=79, score=0.828, total=   4.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=79 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=79, score=0.822, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=79 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=79, score=0.831, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=79 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=79, score=0.824, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=79 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=79, score=0.836, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=80 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=80, score=0.827, total=   4.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=80 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=80, score=0.819, total=   3.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=80 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=80, score=0.827, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=80 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=80, score=0.823, total=   4.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=80 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=80, score=0.833, total=   4.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=81 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=81, score=0.827, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=81 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=81, score=0.827, total=   4.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=81 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=81, score=0.829, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=81 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=81, score=0.821, total=   5.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=81 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=81, score=0.832, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=82 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=82, score=0.827, total=   3.6s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=82 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=82, score=0.824, total=   4.3s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=82 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=82, score=0.829, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=82 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=82, score=0.821, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=82 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=82, score=0.836, total=   4.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=83 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=83, score=0.831, total=   3.4s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=83 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=83, score=0.825, total=   3.7s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=83 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=83, score=0.827, total=   4.0s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=83 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=83, score=0.828, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=83 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=83, score=0.833, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=84 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=84, score=0.826, total=   3.8s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=84 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=84, score=0.824, total=   3.9s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=84 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=84, score=0.830, total=   3.5s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=84 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=84, score=0.822, total=   4.2s
[CV] NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=84 
[CV]  NN__hidden_layer_sizes=8, NN__learning_rate_init=0.05, filter__n=84, score=0.835, total=   3.8s
[Parallel(n_jobs=1)]: Done 420 out of 420 | elapsed: 25.0min finished
In [19]:
df1 = pd.read_csv("./P2/IncomeRF_FI.csv", header=None)
x_data = df1[0] 

plt.figure(figsize = (12,8))
fig, ax1 = plt.subplots()
ax1.plot(x_data, df1[1]* 100.0, label = "Feature Importance" , color = "blue",)
ax1.axvline(x=32 , linestyle = "--", linewidth = 1, color = "k", label = "Optimal Cluster")
ax2 = ax1.twinx() 
ax2.plot(x_data, df1[2]*100.0, label = "Cumulative Importance" , color = "green",)
fig.legend(loc='center left', bbox_to_anchor=(0.5, 0.5))
plt.title("Figure 6.1: Random Forest Feature Importance \nIncome Dataset")
ax1.set_xlabel("Number of Features")
ax1.set_ylabel("Feature Importance Percent")
ax2.set_ylabel("Cumulative Importance Percent")
fig.tight_layout() 

plt.show()
plt.close()
<Figure size 864x576 with 0 Axes>
In [20]:
nn_results = pd.read_csv("./P2/IncomeRF_ANN.csv", header = 'infer')
nn_results = nn_results['mean_test_score'] * 100.0
#nn_train_results_pca = nn_results['mean_train_score'] * 100.0
nn_results.plot( color = 'blue',  label = "Accuracy Percentage" )
plt.axvline(x=32 , linestyle = "--", linewidth = 1, color = "k", label = "Optimal Features (n=32)")

plt.legend(loc='best')
plt.title("Figure 6.2: ANN Accuracy Dimension Reduction using RF\nIncome Dataset")
plt.xlabel("Number of Features")
plt.ylabel("Accuracy Percentage");
plt.show()
plt.close()

clf = gs.best_estimator_
test_score = clf.score(X_test, Y_test)

Part 3: Clustering after reducing dimensions

In [21]:
dimensions_PCA = 48 
dimensions_ICA = 33 
dimensions_RP = 32 
dimensions_RF = 32

rfc = RandomForestClassifier(n_estimators = 100, class_weight = 'balanced', random_state =5, n_jobs = -1)

algo_name = ['PCA', 'ICA', 'RP', 'RF']

filter_ = ImportanceSelect(rfc,dimensions_RF)

algos = [PCA(n_components=dimensions_PCA,random_state=10), 
           FastICA(n_components=dimensions_ICA,random_state=10), 
           SparseRandomProjection(n_components=dimensions_RP,random_state=5),
           ImportanceSelect(rfc,dimensions_RF)]

for i in range(len(algos)):
    if i == 3:
        X2 = algos[i].fit_transform(X_train, Y_train)
    else:   
        X2 = algos[i].fit_transform(X_train)
    data2 = pd.DataFrame(np.hstack((X2,np.atleast_2d(Y_train).T)))
    cols = list(range(data2.shape[1]))
    cols[-1] = 'Class'
    data2.columns = cols
    data2.to_hdf('datasets.hdf','Income_'+algo_name[i],complib='blosc',complevel=9)
In [22]:
#random.seed(55)
titles = ["Figure 7.1: KMeans Clustering with PCA\nIncome Dataset ",
          "Figure 7.2: KMeans Clustering with ICA\nIncome Dataset ",
          "Figure 7.3: KMeans Clustering with RP\nIncome Dataset ",
          "Figure 7.4: KMeans Clustering with RF\nIncome Dataset "]

titles_bic_1 = ["Figure 7.5: Expectation Maximization with PCA\nIncome Dataset ",
          "Figure 7.6: Expectation Maximization with ICA\nIncome Dataset ",
          "Figure 7.7: Expectation Maximization with RP\nIncome Dataset ",
          "Figure 7.8: Expectation Maximization with RF\nIncome Dataset "]

titles_bic_2 = "Cluster Representation"

algo_name = ['PCA', 'ICA', 'RP', 'RF']
for i in range(len(algo_name)):
    temp = pd.read_hdf('datasets.hdf','Income_'+algo_name[i]) 
    #print(temp.columns)
    tempX = temp.drop('Class',1).copy().values
    tempY = temp['Class'].copy().values
    
    tempX = StandardScaler().fit_transform(tempX)
    
    KM_Silhoutte(tempX, tempY, titles[i])
    compute_bic_score(tempX , titles_bic_1[i], titles_bic_2)
Index([      0,       1,       2,       3,       4,       5,       6,       7,
             8,       9,      10,      11,      12,      13,      14,      15,
            16,      17,      18,      19,      20,      21,      22,      23,
            24,      25,      26,      27,      28,      29,      30,      31,
            32,      33,      34,      35,      36,      37,      38,      39,
            40,      41,      42,      43,      44,      45,      46,      47,
       'Class'],
      dtype='object')
For n_clusters = 2 The average silhouette_score is : 0.8215261357021424
For n_clusters = 4 The average silhouette_score is : 0.3785589217345143
For n_clusters = 6 The average silhouette_score is : 0.048072447009243395
For n_clusters = 8 The average silhouette_score is : 0.04530633324794434
For n_clusters = 10 The average silhouette_score is : 0.08734565667552743
For n_clusters = 12 The average silhouette_score is : 0.09957017541125186
For n_clusters = 14 The average silhouette_score is : 0.07786173897738517
For n_clusters = 16 The average silhouette_score is : 0.05420736298294454
For n_clusters = 18 The average silhouette_score is : 0.10268903229937605
For n_clusters = 20 The average silhouette_score is : 0.06945286554344962
For n_clusters = 25 The average silhouette_score is : 0.1378228758182471
For n_clusters = 30 The average silhouette_score is : 0.15818876871088125
CV Type:  spherical  Components:  2  BIC Score:  3745347.508970262
CV Type:  spherical  Components:  4  BIC Score:  3532961.195789733
CV Type:  spherical  Components:  6  BIC Score:  3360142.199403949
CV Type:  spherical  Components:  8  BIC Score:  3081774.0951205264
CV Type:  spherical  Components:  10  BIC Score:  3123888.0092643755
CV Type:  spherical  Components:  12  BIC Score:  2869741.422831554
CV Type:  spherical  Components:  14  BIC Score:  3071478.101352785
CV Type:  spherical  Components:  16  BIC Score:  2853588.3174100206
CV Type:  spherical  Components:  18  BIC Score:  2615582.9497430166
CV Type:  spherical  Components:  20  BIC Score:  2687781.8136283876
CV Type:  spherical  Components:  25  BIC Score:  2470931.75167062
CV Type:  spherical  Components:  30  BIC Score:  2697144.886425882
CV Type:  tied  Components:  2  BIC Score:  4840880.209987787
CV Type:  tied  Components:  4  BIC Score:  4797182.67070697
CV Type:  tied  Components:  6  BIC Score:  4773559.5873399945
CV Type:  tied  Components:  8  BIC Score:  4508735.597861637
CV Type:  tied  Components:  10  BIC Score:  4500535.469897985
CV Type:  tied  Components:  12  BIC Score:  4347956.5581463305
CV Type:  tied  Components:  14  BIC Score:  4090163.3970648698
CV Type:  tied  Components:  16  BIC Score:  3745204.176464526
CV Type:  tied  Components:  18  BIC Score:  3464858.946825357
CV Type:  tied  Components:  20  BIC Score:  3555658.6005753763
CV Type:  tied  Components:  25  BIC Score:  3043886.1970922407
CV Type:  tied  Components:  30  BIC Score:  2137292.85093776
CV Type:  diag  Components:  2  BIC Score:  3399235.566000852
CV Type:  diag  Components:  4  BIC Score:  2922086.198452416
CV Type:  diag  Components:  6  BIC Score:  2782741.3319024527
CV Type:  diag  Components:  8  BIC Score:  2707080.1804200686
CV Type:  diag  Components:  10  BIC Score:  2649194.185964432
CV Type:  diag  Components:  12  BIC Score:  2623756.471771278
CV Type:  diag  Components:  14  BIC Score:  2369300.7315009953
CV Type:  diag  Components:  16  BIC Score:  2233218.823630349
CV Type:  diag  Components:  18  BIC Score:  1969949.4795353825
CV Type:  diag  Components:  20  BIC Score:  1954593.8145316672
CV Type:  diag  Components:  25  BIC Score:  1971297.4436673578
CV Type:  diag  Components:  30  BIC Score:  1980138.9712768537
CV Type:  full  Components:  2  BIC Score:  2166227.399620243
CV Type:  full  Components:  4  BIC Score:  -3949920.431742895
CV Type:  full  Components:  6  BIC Score:  -4116885.7083494337
CV Type:  full  Components:  8  BIC Score:  864792.9015226824
CV Type:  full  Components:  10  BIC Score:  -1548035.1663954924
CV Type:  full  Components:  12  BIC Score:  -6584425.064639831
CV Type:  full  Components:  14  BIC Score:  -1059923.5098059883
CV Type:  full  Components:  16  BIC Score:  -5330335.29175568
CV Type:  full  Components:  18  BIC Score:  -6853798.465888098
CV Type:  full  Components:  20  BIC Score:  -6309757.629066834
CV Type:  full  Components:  25  BIC Score:  -4739147.330329987
CV Type:  full  Components:  30  BIC Score:  -7668024.119338449
Lowest BIC score =  -7668024.119338449
Index([      0,       1,       2,       3,       4,       5,       6,       7,
             8,       9,      10,      11,      12,      13,      14,      15,
            16,      17,      18,      19,      20,      21,      22,      23,
            24,      25,      26,      27,      28,      29,      30,      31,
            32, 'Class'],
      dtype='object')
For n_clusters = 2 The average silhouette_score is : 0.9646678522672069
For n_clusters = 4 The average silhouette_score is : 0.07592079643340581
For n_clusters = 6 The average silhouette_score is : 0.213497825645132
For n_clusters = 8 The average silhouette_score is : 0.072037914310071
For n_clusters = 10 The average silhouette_score is : 0.09107576272590284
For n_clusters = 12 The average silhouette_score is : 0.11480603385717252
For n_clusters = 14 The average silhouette_score is : 0.08893000622045108
For n_clusters = 16 The average silhouette_score is : 0.09890706000954294
For n_clusters = 18 The average silhouette_score is : 0.15731858877060353
For n_clusters = 20 The average silhouette_score is : 0.13820874174539804
For n_clusters = 25 The average silhouette_score is : 0.18629932228570975
For n_clusters = 30 The average silhouette_score is : 0.24555941691171776
CV Type:  spherical  Components:  2  BIC Score:  2794893.5705766655
CV Type:  spherical  Components:  4  BIC Score:  2666504.704999755
CV Type:  spherical  Components:  6  BIC Score:  2505135.568666979
CV Type:  spherical  Components:  8  BIC Score:  2433314.9162478237
CV Type:  spherical  Components:  10  BIC Score:  2477678.9921725243
CV Type:  spherical  Components:  12  BIC Score:  2449741.55101997
CV Type:  spherical  Components:  14  BIC Score:  2191846.4963730136
CV Type:  spherical  Components:  16  BIC Score:  2111020.322212734
CV Type:  spherical  Components:  18  BIC Score:  2209041.0056039
CV Type:  spherical  Components:  20  BIC Score:  2032230.648118387
CV Type:  spherical  Components:  25  BIC Score:  1788065.267311982
CV Type:  spherical  Components:  30  BIC Score:  1783151.3680130304
CV Type:  tied  Components:  2  BIC Score:  3355552.7944893087
CV Type:  tied  Components:  4  BIC Score:  3315336.9927786887
CV Type:  tied  Components:  6  BIC Score:  2955079.498268905
CV Type:  tied  Components:  8  BIC Score:  2873092.346268292
CV Type:  tied  Components:  10  BIC Score:  2610782.9816783145
CV Type:  tied  Components:  12  BIC Score:  2516077.3923429614
CV Type:  tied  Components:  14  BIC Score:  2517671.7818871518
CV Type:  tied  Components:  16  BIC Score:  2282096.8191429265
CV Type:  tied  Components:  18  BIC Score:  2093510.9837368752
CV Type:  tied  Components:  20  BIC Score:  1870023.9688724468
CV Type:  tied  Components:  25  BIC Score:  1433636.4387195671
CV Type:  tied  Components:  30  BIC Score:  1269060.5897719325
CV Type:  diag  Components:  2  BIC Score:  1964898.324173375
CV Type:  diag  Components:  4  BIC Score:  1625543.2344392585
CV Type:  diag  Components:  6  BIC Score:  1398518.6652593124
CV Type:  diag  Components:  8  BIC Score:  1115351.0092439002
CV Type:  diag  Components:  10  BIC Score:  1165605.664829789
CV Type:  diag  Components:  12  BIC Score:  1029835.2559820729
CV Type:  diag  Components:  14  BIC Score:  645806.0061501007
CV Type:  diag  Components:  16  BIC Score:  635139.8559461713
CV Type:  diag  Components:  18  BIC Score:  634607.3325814724
CV Type:  diag  Components:  20  BIC Score:  372034.0620048925
CV Type:  diag  Components:  25  BIC Score:  198045.7241070556
CV Type:  diag  Components:  30  BIC Score:  22892.612583831127
CV Type:  full  Components:  2  BIC Score:  1053404.9812793732
CV Type:  full  Components:  4  BIC Score:  1142072.3111420078
CV Type:  full  Components:  6  BIC Score:  -2772026.1818931396
CV Type:  full  Components:  8  BIC Score:  -1737262.134795803
CV Type:  full  Components:  10  BIC Score:  -3046955.8111493406
CV Type:  full  Components:  12  BIC Score:  -2471378.144497045
CV Type:  full  Components:  14  BIC Score:  -2720490.9954466717
CV Type:  full  Components:  16  BIC Score:  -3285546.2105115782
CV Type:  full  Components:  18  BIC Score:  -3927278.8772990536
CV Type:  full  Components:  20  BIC Score:  -4114532.0709881526
CV Type:  full  Components:  25  BIC Score:  -3843988.980123459
CV Type:  full  Components:  30  BIC Score:  -4420697.066712804
Lowest BIC score =  -4420697.066712804
Index([      0,       1,       2,       3,       4,       5,       6,       7,
             8,       9,      10,      11,      12,      13,      14,      15,
            16,      17,      18,      19,      20,      21,      22,      23,
            24,      25,      26,      27,      28,      29,      30,      31,
       'Class'],
      dtype='object')
For n_clusters = 2 The average silhouette_score is : 0.16022875673042225
For n_clusters = 4 The average silhouette_score is : 0.15969012342995884
For n_clusters = 6 The average silhouette_score is : 0.15536780189221971
For n_clusters = 8 The average silhouette_score is : 0.09753844411831471
For n_clusters = 10 The average silhouette_score is : 0.0952952534361466
For n_clusters = 12 The average silhouette_score is : 0.10272294301390972
For n_clusters = 14 The average silhouette_score is : 0.10893886515134017
For n_clusters = 16 The average silhouette_score is : 0.09367900790452478
For n_clusters = 18 The average silhouette_score is : 0.12388388890315521
For n_clusters = 20 The average silhouette_score is : 0.12790969312503994
For n_clusters = 25 The average silhouette_score is : 0.1332935713527438
For n_clusters = 30 The average silhouette_score is : 0.15967340153296714
CV Type:  spherical  Components:  2  BIC Score:  2469005.22954279
CV Type:  spherical  Components:  4  BIC Score:  2210724.047029868
CV Type:  spherical  Components:  6  BIC Score:  2124578.46914328
CV Type:  spherical  Components:  8  BIC Score:  1990241.9800328682
CV Type:  spherical  Components:  10  BIC Score:  2026840.3656783383
CV Type:  spherical  Components:  12  BIC Score:  1915772.7693289593
CV Type:  spherical  Components:  14  BIC Score:  1842523.769251369
CV Type:  spherical  Components:  16  BIC Score:  1805533.5327718926
CV Type:  spherical  Components:  18  BIC Score:  1816137.4260121973
CV Type:  spherical  Components:  20  BIC Score:  1732167.9627771338
CV Type:  spherical  Components:  25  BIC Score:  1767862.1836157956
CV Type:  spherical  Components:  30  BIC Score:  1580775.6728847506
CV Type:  tied  Components:  2  BIC Score:  2923609.346985227
CV Type:  tied  Components:  4  BIC Score:  2885791.2198778247
CV Type:  tied  Components:  6  BIC Score:  2819918.7557755043
CV Type:  tied  Components:  8  BIC Score:  2762697.7251161733
CV Type:  tied  Components:  10  BIC Score:  2717207.7802632772
CV Type:  tied  Components:  12  BIC Score:  2622475.5699871015
CV Type:  tied  Components:  14  BIC Score:  2587420.648719321
CV Type:  tied  Components:  16  BIC Score:  2519339.441507217
CV Type:  tied  Components:  18  BIC Score:  2470754.053737411
CV Type:  tied  Components:  20  BIC Score:  2397490.8458128436
CV Type:  tied  Components:  25  BIC Score:  2256154.7829355323
CV Type:  tied  Components:  30  BIC Score:  2040743.6783353519
CV Type:  diag  Components:  2  BIC Score:  1561396.6396238785
CV Type:  diag  Components:  4  BIC Score:  -34207.82500506729
CV Type:  diag  Components:  6  BIC Score:  -76772.76057003971
CV Type:  diag  Components:  8  BIC Score:  -34742.3297100728
CV Type:  diag  Components:  10  BIC Score:  -716663.9759328412
CV Type:  diag  Components:  12  BIC Score:  -1235531.5963014266
CV Type:  diag  Components:  14  BIC Score:  -1190866.7878785722
CV Type:  diag  Components:  16  BIC Score:  -1225442.4099257055
CV Type:  diag  Components:  18  BIC Score:  -1832656.3498493833
CV Type:  diag  Components:  20  BIC Score:  -1482871.5102674135
CV Type:  diag  Components:  25  BIC Score:  -1419408.6493819994
CV Type:  diag  Components:  30  BIC Score:  -2373935.433847721
CV Type:  full  Components:  2  BIC Score:  -68776.4115549257
CV Type:  full  Components:  4  BIC Score:  -656825.6801409031
CV Type:  full  Components:  6  BIC Score:  -1933115.5191570814
CV Type:  full  Components:  8  BIC Score:  -1905070.445373736
CV Type:  full  Components:  10  BIC Score:  -1882612.208893617
CV Type:  full  Components:  12  BIC Score:  -3229480.4135010685
CV Type:  full  Components:  14  BIC Score:  -3070459.008296061
CV Type:  full  Components:  16  BIC Score:  -2998327.881747128
CV Type:  full  Components:  18  BIC Score:  -3902800.0141554875
CV Type:  full  Components:  20  BIC Score:  -2810025.759273552
CV Type:  full  Components:  25  BIC Score:  -3838681.8899121713
CV Type:  full  Components:  30  BIC Score:  -4467423.763978708
Lowest BIC score =  -4467423.763978708
Index([      0,       1,       2,       3,       4,       5,       6,       7,
             8,       9,      10,      11,      12,      13,      14,      15,
            16,      17,      18,      19,      20,      21,      22,      23,
            24,      25,      26,      27,      28,      29,      30,      31,
       'Class'],
      dtype='object')
For n_clusters = 2 The average silhouette_score is : 0.12169582291600911
For n_clusters = 4 The average silhouette_score is : 0.14022899111373643
For n_clusters = 6 The average silhouette_score is : 0.15746669528328971
For n_clusters = 8 The average silhouette_score is : 0.1805351940015467
For n_clusters = 10 The average silhouette_score is : 0.14735435240340997
For n_clusters = 12 The average silhouette_score is : 0.15222477543125723
For n_clusters = 14 The average silhouette_score is : 0.1484213859648749
For n_clusters = 16 The average silhouette_score is : 0.18957531078729173
For n_clusters = 18 The average silhouette_score is : 0.1781551968468422
For n_clusters = 20 The average silhouette_score is : 0.19399015395613495
For n_clusters = 25 The average silhouette_score is : 0.20856161952279353
For n_clusters = 30 The average silhouette_score is : 0.23067453495515897
CV Type:  spherical  Components:  2  BIC Score:  3155204.028818856
CV Type:  spherical  Components:  4  BIC Score:  3025094.1382223754
CV Type:  spherical  Components:  6  BIC Score:  2946091.7199722263
CV Type:  spherical  Components:  8  BIC Score:  2848359.6696788864
CV Type:  spherical  Components:  10  BIC Score:  2776518.417764349
CV Type:  spherical  Components:  12  BIC Score:  2619968.1231891536
CV Type:  spherical  Components:  14  BIC Score:  2621102.596302614
CV Type:  spherical  Components:  16  BIC Score:  2515116.1417451296
CV Type:  spherical  Components:  18  BIC Score:  2534939.2915649754
CV Type:  spherical  Components:  20  BIC Score:  2344056.39136436
CV Type:  spherical  Components:  25  BIC Score:  2255135.688826514
CV Type:  spherical  Components:  30  BIC Score:  2185987.6409853664
CV Type:  tied  Components:  2  BIC Score:  2064502.2242727114
CV Type:  tied  Components:  4  BIC Score:  1546255.483639819
CV Type:  tied  Components:  6  BIC Score:  1442988.2537666245
CV Type:  tied  Components:  8  BIC Score:  1325196.940344845
CV Type:  tied  Components:  10  BIC Score:  798976.2275015559
CV Type:  tied  Components:  12  BIC Score:  1004792.2816102488
CV Type:  tied  Components:  14  BIC Score:  1088544.4989831918
CV Type:  tied  Components:  16  BIC Score:  -237818.25770603583
CV Type:  tied  Components:  18  BIC Score:  -987250.4294325517
CV Type:  tied  Components:  20  BIC Score:  -922237.3717277922
CV Type:  tied  Components:  25  BIC Score:  -903693.9938874283
CV Type:  tied  Components:  30  BIC Score:  -962263.6691750985
CV Type:  diag  Components:  2  BIC Score:  922523.4044359996
CV Type:  diag  Components:  4  BIC Score:  -138700.1425964475
CV Type:  diag  Components:  6  BIC Score:  -988054.457217085
CV Type:  diag  Components:  8  BIC Score:  -2096796.990857835
CV Type:  diag  Components:  10  BIC Score:  -2665228.039344091
CV Type:  diag  Components:  12  BIC Score:  -3234841.4290826647
CV Type:  diag  Components:  14  BIC Score:  -4052055.7650737884
CV Type:  diag  Components:  16  BIC Score:  -4127713.1138305124
CV Type:  diag  Components:  18  BIC Score:  -4766081.457214911
CV Type:  diag  Components:  20  BIC Score:  -4659057.469583905
CV Type:  diag  Components:  25  BIC Score:  -5258364.160233665
CV Type:  diag  Components:  30  BIC Score:  -5677458.393623538
CV Type:  full  Components:  2  BIC Score:  418826.656334546
CV Type:  full  Components:  4  BIC Score:  -820295.0824815511
CV Type:  full  Components:  6  BIC Score:  -2042795.9160205168
CV Type:  full  Components:  8  BIC Score:  -1764055.5297574208
CV Type:  full  Components:  10  BIC Score:  -3569718.6112716575
CV Type:  full  Components:  12  BIC Score:  -3863197.571373438
CV Type:  full  Components:  14  BIC Score:  -4430588.0835696645
CV Type:  full  Components:  16  BIC Score:  -4752320.395185009
CV Type:  full  Components:  18  BIC Score:  -5027715.870817529
CV Type:  full  Components:  20  BIC Score:  -5358760.149064392
CV Type:  full  Components:  25  BIC Score:  -5695101.185917668
CV Type:  full  Components:  30  BIC Score:  -6171014.81591099
Lowest BIC score =  -6171014.81591099
In [32]:
rc_results = pd.read_csv("./P2/IncomeRP_Reconstruction.csv", header = 'infer')
rc_results = rc_results['Reconstruction'] * 100.0
rc_results.plot( color = 'orange',  label = "Reconstruction Error" )
plt.axvline(x=33 , linestyle = "--", linewidth = 1, color = "k", label = "Optimal Components (n=33)")

plt.legend(loc='best')
plt.title("Figure 5.2: Random Projection Reconstruction Error\nIncome Dataset")
plt.xlabel("Random Components")
plt.ylabel("Reconstruction Error %");
plt.show()
plt.close()
In [ ]: